Localization refers to the process by which an object's location is determined. Outdoor localization, for example, through global positioning systems (GPS) is prevalent. However, indoor localization is not common, partly due to the spatial granularity requirements that place a burden on a system to achieve fairly high location accuracy. For instance, while a five-meter location error outdoors may still indicate the same street, a five-meter location error in an indoor environment may mean an entirely different room in a building or two different aisles in a grocery store, which would render an inventory-management application that needs aisle-level precision inoperable.
While high precision may be attainable with pervasive WiFi systems, this comes at what may be prohibitively high cost, mostly in the form of meticulous signal calibration. Also, such calibration is not necessarily a one-time cost since radio frequency (RF) fingerprints could change, for example, due to changes in layout and objects in the indoor environment. Attempting to simplify the calibration process in the related art has led to significantly reduced location accuracy. This tradeoff between accuracy and calibration overhead has been an important challenge to the development of an accurate indoor localization system with low calibration overhead.